Current methods of determining motor threshold (MT) position and stimulation levels for transcranial magnetic stimulation (TMS) studies rely on visual observation and interpretation of induced twitching of the thumb (i.e. abductor pollicis brevis) or by electromyography (EMG), which involves observation and interpretation of electrical response waveforms. In particular, a common method is to stimulate the motor cortex, observe thumb twitch or observe when the desired EMG signal exceeds a threshold value (i.e. motor evoked potential, MEP) as the stimulation level is manually adjusted. Both techniques are time consuming and highly dependent upon the skills and training of the practitioner. A more automated technique is desired that is not so operator dependent and time consuming. Such a technique should ideally provide simple feedback to the operator or may be used to close the loop to automate the motor threshold position determination process.
It would be advantageous to more directly determine desired levels for stimulating non-motor areas of the brain (e.g. prefrontal cortex); however, such techniques have not yet been developed. Direct measurement of evoked potential for non-motor areas using EMG techniques has been proposed by Sarah Lisanby, M.D. Unfortunately, direct measurement of evoked potential is not straight-forward since neurons that are directly stimulated are not readily accessible with non-invasive techniques. Functional magnetic resonance imaging (fMRI) or positron emission tomography may be used to observe levels of neuronal stimulation, but these methods are expensive, would require TMS procedures to be performed at a facility with this equipment, and are logistically impractical for routine clinical TMS therapy. Indirect methods such as observation and interpretation of electroencephalogram (EEG) signals may be possible and are generally described herein.
Numerous search algorithms to determine the optimal stimulation level have also been proposed and tested clinically. For example, a procedure often used in TMS research estimates the motor threshold at a stimulus strength where 5 successes are observed within 10 stimuli. Another approach estimates the arithmetic mean of an upper threshold (smallest stimulus strength with 10 successes in 10 trials) and a lower threshold (largest stimulus strength with no success in 10 trials). Professor Friedemann Awiszus (Magdeburg, Germany) describes another search strategy for threshold estimation called the PEST (parameter estimation by sequential testing) algorithm in a publication titled “TMS and Threshold Hunting.” The PEST algorithm uses adaptive threshold hunting to estimate the threshold continuously throughout the stimulus sequence where the stimulus strength that is to be used for the next stimulus is calculated from the information obtained from the previous stimuli.
The block diagram of FIG. 1 shows the typical motor threshold level determination procedure used today. In this case the operator 10 operates a TMS stimulator 20 that provides pulses to a stimulation magnet 30 for application of TMS signals to a patient 40. The operator 10 receives direct visual feedback from the patient 40 or from an EMG display (not shown). The stimulation level and/or position is then adjusted manually by the operator 10 and the process repeated until a level is attained where half of the stimulation pulses result in a valid detected movement of the thumb. This approach can be augmented by employing an offline search algorithm 50, such as the PEST algorithm, to aid in selecting stimulation values based on prior responses. Use of the PEST algorithm is reflected by the diagram in prior art FIG. 2.
It is known to monitor patient movement to detect evidence of seizure activity. For example, Gliner discloses in U.S. Patent Publication No. US 2003/0074032 A1 a neural stimulation system that uses a sensing unit to detect evidence of seizure or other collateral neural activity in response to an applied neural stimulation. The sensing unit may be an EEG monitoring device, a cerebral blood flow (CBF) monitor, a neural tissue oxygenation analysis device, or an electromyography device. In one embodiment, the monitoring device may also comprise a set of motion detectors, strain gauges, and/or accelerometers configured to detect or monitor one or more types of patient movements that may be indicative of seizure activity. However, Gliner does not suggest how such a system may be used to detect motor threshold positions and levels and does not suggest correlating induced movement in the patient with a stimulation pulse to find the motor threshold position. On the contrary, the Gliner system stops the application of neural stimulation when a potential seizure or other collateral neural activity is detected. Moreover, Gliner is focused on seizure detection/prevention which is a very different purpose and involves detecting very different signal characteristics than proposed in accordance with the present invention. In the present application, the inventor is interested in detecting and observing “normal” levels of nerve stimulation, even though the stimulation is induced with a magnetic field. Seizures are a different phenomenon that typically occur at very much higher levels of magnetic stimulation (e.g. >2 times the MT level).
None of the prior art techniques known to the inventor suggests how to directly detect induced physical movement and how to correlate detected induced movement with TMS stimulation levels in order to determine TMS treatment stimulation levels or motor threshold. Prior art techniques do not describe methods of separately determining cortical depth and levels of neuronal excitability for the purpose of setting TMS stimulation levels. The prior art also does not teach techniques of determining TMS stimulation levels by observation and analysis of indirect signals such as EEG and its derivatives. The present invention addresses these needs in the art.